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      The Association of Income with Health Behavior Change and Disease Monitoring among Patients with Chronic Disease

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          Abstract

          Background

          Management of chronic diseases requires patients to adhere to recommended health behavior change and complete tests for monitoring. While studies have shown an association between low income and lack of adherence, the reasons why people with low income may be less likely to adhere are unclear. We sought to determine the association between household income and receipt of health behavior change advice, adherence to advice, receipt of recommended monitoring tests, and self-reported reasons for non-adherence/non-receipt.

          Methods

          We conducted a population-weighted survey, with 1849 respondents with cardiovascular-related chronic diseases (heart disease, hypertension, diabetes, stroke) from Western Canada (n = 1849). We used log-binomial regression to examine the association between household income and the outcome variables of interest: receipt of advice for and adherence to health behavior change (sodium reduction, dietary improvement, increased physical activity, smoking cessation, weight loss), reasons for non-adherence, receipt of recommended monitoring tests (cholesterol, blood glucose, blood pressure), and reasons for non-receipt of tests.

          Results

          Behavior change advice was received equally by both low and high income respondents. Low income respondents were more likely than those with high income to not adhere to recommendations regarding smoking cessation (adjusted prevalence rate ratio (PRR): 1.55, 95%CI: 1.09–2.20), and more likely to not receive measurements of blood cholesterol (PRR: 1.72, 95%CI 1.24–2.40) or glucose (PRR: 1.80, 95%CI: 1.26–2.58). Those with low income were less likely to state that non-adherence/non-receipt was due to personal choice, and more likely to state that it was due to an extrinsic factor, such as cost or lack of accessibility.

          Conclusions

          There are important income-related differences in the patterns of health behavior change and disease monitoring, as well as reasons for non-adherence or non-receipt. Among those with low income, adherence to health behavior change and monitoring may be improved by addressing modifiable barriers such as cost and access.

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          Most cited references 39

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          Effects of exercise on glycemic control and body mass in type 2 diabetes mellitus: a meta-analysis of controlled clinical trials.

          Exercise is widely perceived to be beneficial for glycemic control and weight loss in patients with type 2 diabetes. However, clinical trials on the effects of exercise in patients with type 2 diabetes have had small sample sizes and conflicting results. To systematically review and quantify the effect of exercise on glycosylated hemoglobin (HbA(1c)) and body mass in patients with type 2 diabetes. Database searches of MEDLINE, EMBASE, Sport Discuss, Health Star, Dissertation Abstracts, and the Cochrane Controlled Trials Register for the period up to and including December 2000. Additional data sources included bibliographies of textbooks and articles identified by the database searches. We selected studies that evaluated the effects of exercise interventions (duration >/=8 weeks) in adults with type 2 diabetes. Fourteen (11 randomized and 3 nonrandomized) controlled trials were included. Studies that included drug cointerventions were excluded. Two reviewers independently extracted baseline and postintervention means and SDs for the intervention and control groups. The characteristics of the exercise interventions and the methodological quality of the trials were also extracted. Twelve aerobic training studies (mean [SD], 3.4 [0.9] times/week for 18 [15] weeks) and 2 resistance training studies (mean [SD], 10 [0.7] exercises, 2.5 [0.7] sets, 13 [0.7] repetitions, 2.5 [0.4] times/week for 15 [10] weeks) were included in the analyses. The weighted mean postintervention HbA(1c) was lower in the exercise groups compared with the control groups (7.65% vs 8.31%; weighted mean difference, -0.66%; P<.001). The difference in postintervention body mass between exercise groups and control groups was not significant (83.02 kg vs 82.48 kg; weighted mean difference, 0.54; P =.76). Exercise training reduces HbA(1c) by an amount that should decrease the risk of diabetic complications, but no significantly greater change in body mass was found when exercise groups were compared with control groups.
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            Mortality risk reduction associated with smoking cessation in patients with coronary heart disease: a systematic review.

            As more interventions become available for the treatment of coronary heart disease (CHD), policy makers and health practitioners need to understand the benefits of each intervention, to better determine where to focus resources. This is particularly true when a patient with CHD quits smoking. To conduct a systematic review to determine the magnitude of risk reduction achieved by smoking cessation in patients with CHD. Nine electronic databases were searched from start of database to April 2003, supplemented by cross-checking references, contact with experts, and with large international cohort studies (identified by the Prospective Studies Collaboration). Prospective cohort studies of patients who were diagnosed with CHD were included if they reported all-cause mortality and had at least 2 years of follow-up. Smoking status had to be measured after CHD diagnosis to ascertain quitting. Two reviewers independently assessed studies to determine eligibility, quality assessment of studies, and results, and independently carried out data extraction using a prepiloted, standardized form. From the literature search, 665 publications were screened and 20 studies were included. Results showed a 36% reduction in crude relative risk (RR) of mortality for patients with CHD who quit compared with those who continued smoking (RR, 0.64; 95% confidence interval [CI], 0.58-0.71). Results from individual studies did not vary greatly despite many differences in patient characteristics, such as age, sex, type of CHD, and the years in which studies took place. Adjusted risk estimates did not differ substantially from crude estimates. Many studies did not adequately address quality issues, such as control of confounding, and misclassification of smoking status. However, restriction to 6 higher-quality studies had little effect on the estimate (RR, 0.71; 95% CI, 0.65-0.77). Few studies included large numbers of elderly persons, women, ethnic minorities, or patients from developing countries. Quitting smoking is associated with a substantial reduction in risk of all-cause mortality among patients with CHD. This risk reduction appears to be consistent regardless of age, sex, index cardiac event, country, and year of study commencement.
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              Socioeconomic status and health: how education, income, and occupation contribute to risk factors for cardiovascular disease.

              Socioeconomic status (SES) is usually measured by determining education, income, occupation, or a composite of these dimensions. Although education is the most commonly used measure of SES in epidemiological studies, no investigators in the United States have conducted an empirical analysis quantifying the relative impact of each separate dimension of SES on risk factors for disease. Using data on 2380 participants from the Stanford Five-City Project (85% White, non-Hispanic), we examined the independent contribution of education, income, and occupation to a set of cardiovascular disease risk factors (cigarette smoking, systolic and diastolic blood pressure, and total and high-density lipoprotein cholesterol). The relationship between these SES measures and risk factors was strongest and most consistent for education, showing higher risk associated with lower levels of education. Using a forward selection model that allowed for inclusion of all three SES measures after adjustment for age and time of survey, education was the only measure that was significantly associated with the risk factors (P less than .05). If economics or time dictate that a single parameter of SES be chosen and if the research hypothesis does not dictate otherwise, higher education may be the best SES predictor of good health.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2014
                10 April 2014
                : 9
                : 4
                Affiliations
                [1 ]Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
                [2 ]Department of Medicine, University of Calgary, Calgary, Alberta, Canada
                [3 ]Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
                [4 ]Statistics Canada, Health Analysis Division, Ottawa, Ontario, Canada
                [5 ]Institute of Public Health, University of Calgary, Calgary, Alberta, Canada
                [6 ]Libin Cardiovascular Institute, University of Calgary, Calgary, Alberta, Canada
                [7 ]Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
                [8 ]Faculty of Nursing, University of Calgary, Calgary, Alberta, Canada
                [9 ]Department of Psychology, University of Calgary, Calgary, Alberta, Canada
                Oregon Health & Science University, United States of America
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: BRH BJM MT. Analyzed the data: DJTC BRH PER RGW. Wrote the paper: DJTC PER BJM MT CS RGW DH KKS TC BRH. Contributed to survey design as consultants: KKS TC CS. Oversaw the administration of the survey and collection of data: CS. Primarily responsible for the analysis in this manuscript: DJTC BRH.

                Article
                PONE-D-13-43804
                10.1371/journal.pone.0094007
                3983092
                24722618

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Pages: 8
                Funding
                This project was supported by a Team Grant from Alberta Innovates Health Solutions. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors can declare that their provision of funding does not alter their adherence to all the PLOS ONE policies on data sharing and materials.
                Categories
                Research Article
                Biology and Life Sciences
                Nutrition
                Medicine and Health Sciences
                Cardiology
                Endocrinology
                Diabetic Endocrinology
                Epidemiology
                Cardiovascular Disease Epidemiology
                Social Epidemiology
                Health Care
                Health Care Providers
                Medical Doctors
                Physicians
                Health Care Policy
                Health Education and Awareness
                Health Services Research
                Psychological and Psychosocial Issues
                Socioeconomic Aspects of Health
                Public and Occupational Health
                Behavioral and Social Aspects of Health
                Sports and Exercise Medicine
                Vascular Medicine
                Blood Pressure
                Hypertension
                Coronary Artery Disease
                Stroke
                Research and Analysis Methods
                Research Design
                Survey Research
                Survey Methods
                Clinical Research Design
                Cross-Sectional Studies

                Uncategorized

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